Quench-based magnet sorting at the MEB presented by L. Bottura Workshop on Beam generated heat deposition and quench levels for LHC magnets March 3 rd, 2005 ? ?

Quench classes presently used at MEB  3 and 1/2 classes, based on training and electrical characteristics: Golden (bonus magnets, do not detrain ? preferably use in hot regions) N ramp (9 T)  3 and[electrically sound] Silver-plus (nearly as good as golden magnets ? preferably use in hot regions) N ramp (9 T) > 3 and N ramp (8.75 T)  2and[electrically sound] Silver (normal magnets) N ramp (9 T)  9 and N ramp (8.4 T)  2 during first test or N ramp (9 T)  9 and N ramp (8.4 T)  1 after thermal cycleand [electrically sound] or N ramp (9 T)  9 and N ramp (8.6 T)  2 after thermal cycle Reserve (bound to train/detrain, preferably use in shielded regions) training as defined for G, S+, S, but shows detraining, or high splice resistance or deviation from nominal cable margin at 4.5 K following the definition of A. Siemko, P. Pugnat and E. Floch

Rationale of sorting for optimal performance  Mission: place the magnets so that the highest possible field is reached for LHC operation  A sorting that aims at optimal quench performance is possible if, and only if: we know the perturbation spectrum (measured by the energy and power input to the magnet) locations with highest beam induced loss locations with highest operating temperature and we know the margin to quench (measured by the energy and power margin of the magnet)

Questions on sorting for optimal performance  Two questions must be answered, for this sorting to be effective: Q1: is the energy and power input to the magnet sufficiently known, i.e. what is the worst 10 % of the machine ? Q2: does training, as executed today, give an unambiguous measurement of the energy and power margin of the magnet, i.e. through the cable temperature margin (as we have no other stability data) ?

Cable temperature margin vs. first quench and classes  There is no correlation between the cable temperature margin and the first quench, nor the class  The answer to Q2 is NO  In other terms, there is no reason to sort for optimal performance based on the information we have available today courtesy of M. Pojer

Rationale of sorting for maximum robustness  Mission: place the magnets so that in case of quench the LHC performance at the next ramp is not decreased  A sorting that aims at maximum robustness is possible if, and only if, we know the likelihood to loose performance after a quench (measured by de-training ?) and we know the likelihood to quench in a given tunnel slot

Questions on sorting for maximum robustness  Two questions must be answered, for this sorting to be effective: Q1: is a magnet more likely to quench when placed in a hot tunnel location ? NOTE: this problem is the same as posed to the sorting for optimal performance… Q2: does training, as executed today, give an unambiguous measurement of the detraining potential ? I am missing a clear answer to both questions

The MEB installation algorithm as used today  Logic: form pairs of magnets that compensate deviations of field errors from average in the sector (compensated by the correctors) to minimise the effect of the rms exception rules (by priority): satisfy hardware type constraints (diode, spool) no more than 1 magnet with defective T-sensor per cell, in locations that can be accepted by cryo-control power-R magnets in mid-cell locations geo-MC, geo-SL, geo-SR magnets (with out-of-tolerance geometry) in suitable locations in arc cells (mid-cell, right or left of a MQ) geo-G magnets in DS cells power-G and power-S+ magnets in priority to DS, power-S otherwise not more than 3 magnets in a cell with out-of-tolerance b1 and/or a1 courtesy of S. Fartoukh, power class from A. Siemko References:Proc. of LHC Workshop Chamonix-XIII, pp , 2004 Proc. of EPAC 2004, pp , 2004

Conclusions  should we sort magnets using information from training ?  what are the criteria ? number of quenches to nominal/ultimate training memory after thermal cycle extrapolated temperature margin  can we reliably define performance classes ?  where do they belong ? locations with largest heat load locations with highest temperature